T-NeRF, Nerfies, and HyperNeRF share the exact same training setup since they are implemented within our codebase.
We follow the hyper-parameters specified in their official repositories.
We use a batch size \(B = 6144\)  for a total number of iterations \(N = 2.5 \times 10^5\) , optimized by ADAM [1]} with an initial learning rate \(\eta = 1 \times 10^{-3}\)  exponentially decayed to \(1 \times ^{-4}\)  at the end.
We use this training recipe for all of our experiments across all datasets.
On 4 NVIDIA RTX A4000 or 2 NVIDIA A100 GPUs with \(24{GB}\)  memory, it takes roughly 12 hours to train a T-NeRF and 24 hours to train a Nerfies or a HyperNeRF.
In Table REF , we show that our codebase reproduces the numbers from the original papers and official repositories.
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